311 requests¶
First, we'll look at the 311 calls dataset. This dataset in its raw form is quite large (7M rows, and 43 columns). To avoid running into memory issues, and keep runtimes reasonable, we can sample a chunk of the dataset instead of reading all of it. We can increase the sample size after the data exploration phase, if a need arises - such as if an analysis we're interested in requires more data points.
To analyze the 311 Service requests dataset, we will follow a structured approach to ingest, process, and aggregate the data, focusing on understanding the inbound call patterns. Here's an outline of the steps we'll take, the potential challenges we might encounter, and how we'll overcome them, followed by an analysis and visualization of our findings. The detailed steps described below are standard steps for data analysis, like we'd find in any reference material.
Step 1: Data Ingestion¶
Action: Load the dataset using Pandas.\
Challenge: The file size is very large, and leads to memory issues.\
Solution: Use chunking and randomly sample data points so we have span all the timeperiods.
Step 2: Preliminary Exploration¶
Action: Perform an initial exploration to understand the dataset's structure, including the number of records, features, missing values, and data types.\
Challenge: Identifying relevant features and handling missing or inconsistent data.\
Solution: Use descriptive statistics and visualization tools to assess data quality, and apply imputation or removal of missing data as appropriate.
Step 3: Data Cleaning¶
Action: Clean the dataset by handling missing values, correcting data types (e.g., converting timestamps to datetime objects), and removing duplicates.\
Challenge: Ensuring accurate data type conversions and dealing with outliers.\
Solution: Validate conversions through sample checks and use statistical methods to identify and handle outliers.
Step 4: Data Aggregation¶
Action: Aggregate the data to identify patterns, such as call volume over time, most common service requests, and geographic distribution of calls.\
Challenge: Choosing the right level of aggregation to reveal meaningful patterns without oversimplification.\
Solution: Experiment with different aggregation levels (e.g., daily, monthly, by neighborhood) and metrics (e.g., count, mean) to find the most insightful views.
Step 5: Analysis and Visualization¶
Action: Analyze aggregated data to uncover trends, seasonal patterns, and anomalies. Visualize findings using charts and maps.\
Challenge: Making complex data understandable and visually engaging.\
Solution: Use a combination of visualization techniques, such as time series plots, bar charts, heatmaps, and geographic maps.
Step 6: Implications and Insights¶
Action: Interpret patterns to infer implications for city management, resource allocation, and policy making.\
Challenge: Translating data patterns into actionable insights.\
Solution: Combine data analysis with domain knowledge to provide recommendations or insights.
Let's start by ingesting the dataset and performing a preliminary exploration to understand its structure. We'll look at the number of records, features, and get a sense of the data we're dealing with. Then, we'll proceed with the subsequent steps based on our initial findings.
We'll jump right into it by importing libraries. Then we'll load and examine the data to understand its structure and contents. This examination will allow us to identify the best ways to aggregate the 311 records and describe the characteristics of the inbound call patterns. Let's start by loading the data and taking a look at the first few rows.
